Artificial intelligence-based network advisor

A computer-implemented method, comprising:

• receiving, at a data adaptor platform executing on one or more computing nodes, performance data regarding user device and network components of a wireless carrier network from multiple data sources, the performance data including one or more of network component performance data, user device performance data, social media data, alarm data, trouble ticket data, or key performance indicator data;
• identifying at a network fix application executing on the one or more computing nodes, at least one issue affecting the network performance in one or more poor performing geographical areas, wherein the issue negatively impacts the performance data to fall below a predetermined threshold;
• analyzing, via the network fix application executing on the one or more computing nodes, the performance data using a trained machine learning model to determine a predicted root cause for the issue affecting the network performance, the trained machine learning model employing multiple types of machine learning algorithms to analyze the performance data;
• and
• providing, via the network fix application executing on the one or more computing nodes, at least one of the predicted root cause for presentation via a user interface;
• refining the machine learning model based on user feedback regarding an accuracy of the predicted root cause, the refining including retraining the machine learning model based on at least one of a training corpus that is modified based on the feedback or one or more modified algorithm selection rules;
• providing a network fix prioritization for each of the issue to implement a solution that resolves the predicted root causes for the issue;
• and
• refining the machine learning model based on user feedback regarding an accuracy of the network fix prioritization, the refining including retraining the machine learning model based on at least one of the training corpus that is modified based on the feedback or one or more modified algorithm selection rules.

An apparatus comprising:

• one or more processors, wherein the apparatus is configured to use the one or more processors to perform acts comprising:
• receiving performance data regarding user device and network components of a wireless carrier network from multiple data sources, the performance data including one or more of network component performance data, user device performance data, social media data, alarm data, trouble ticket data, or key performance indicator data;
• identifying at least one issue affecting the network performance in one or more poor performing geographical areas, wherein the issue negatively impacts the performance data to fall below a predetermined threshold;
• analyzing the performance data using a trained machine learning model to determine a predicted root cause for the issue affecting the network performance, the trained machine learning model employing multiple types of machine learning algorithms to analyze the performance data;
• providing at least one of the predicted root cause for presentation via a user interface;
• refining the machine learning model based on user feedback regarding an accuracy of the predicted root cause, the refining including retraining the machine learning model based on at least one of a training corpus that is modified based on the feedback or one or more modified algorithm selection rules;
• providing a network fix prioritization for each of the issue to implement a solution that resolves the predicted root causes for the issue;
• and
• refining the machine learning model based on user feedback regarding an accuracy of the network fix prioritization, the refining including retraining the machine learning model based on at least one of the training corpus that is modified based on the feedback or one or more modified algorithm selection rules.